Data Communications In A Parallel Active Messaging Interface Of A Parallel Computer

ABSTRACT

Data communications in a parallel active messaging interface (‘PAMI’) of a parallel computer, the PAMI composed of data communications endpoints, each endpoint including a specification of data communications parameters for a thread of execution on a compute node, including specifications of a client, a context, and a task, endpoints coupled for data communications through the PAMI and through data communications resources, including receiving in an origin endpoint of the PAMI a SEND instruction, the SEND instruction specifying a transmission of transfer data from the origin endpoint to a first target endpoint; transmitting from the origin endpoint to the first target endpoint a Request-To-Send (‘RTS’) message advising the first target endpoint of the location and size of the transfer data; assigning by the first target endpoint to each of a plurality of target endpoints separate portions of the transfer data; and receiving by the plurality of target endpoints the transfer data.

STATEMENT REGARDING FEDERALLY SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under Contract No.B544331 awarded by the Department of Energy. The Government has certainrights in this invention.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The field of the invention is data processing, or, more specifically,methods, apparatus, and products for data communications in a parallelactive messaging interface (‘PAMI’) of a parallel computer.

2. Description of Related Art

The development of the EDVAC computer system of 1948 is often cited asthe beginning of the computer era. Since that time, computer systemshave evolved into extremely complicated devices. Today's computers aremuch more sophisticated than early systems such as the EDVAC. Computersystems typically include a combination of hardware and softwarecomponents, application programs, operating systems, processors, buses,memory, input/output devices, and so on. As advances in semiconductorprocessing and computer architecture push the performance of thecomputer higher and higher, more sophisticated computer software hasevolved to take advantage of the higher performance of the hardware,resulting in computer systems today that are much more powerful thanjust a few years ago.

Parallel computing is an area of computer technology that hasexperienced advances. Parallel computing is the simultaneous executionof the same application (split up and specially adapted) on multipleprocessors in order to obtain results faster. Parallel computing isbased on the fact that the process of solving a problem usually can bedivided into smaller jobs, which may be carried out simultaneously withsome coordination.

Parallel computers execute parallel algorithms. A parallel algorithm canbe split up to be executed a piece at a time on many differentprocessing devices, and then put back together again at the end to get adata processing result. Some algorithms are easy to divide up intopieces. Splitting up the job of checking all of the numbers from one toa hundred thousand to see which are primes could be done, for example,by assigning a subset of the numbers to each available processor, andthen putting the list of positive results back together. In thisspecification, the multiple processing devices that execute theindividual pieces of a parallel program are referred to as ‘computenodes.’ A parallel computer is composed of compute nodes and otherprocessing nodes as well, including, for example, input/output (‘I/O’)nodes, and service nodes.

Parallel algorithms are valuable because it is faster to perform somekinds of large computing jobs via a parallel algorithm than it is via aserial (non-parallel) algorithm, because of the way modern processorswork. It is far more difficult to construct a computer with a singlefast processor than one with many slow processors with the samethroughput. There are also certain theoretical limits to the potentialspeed of serial processors. On the other hand, every parallel algorithmhas a serial part and so parallel algorithms have a saturation point.After that point adding more processors does not yield any morethroughput but only increases the overhead and cost.

Parallel algorithms are designed also to optimize one more resource thedata communications requirements among the nodes of a parallel computer.There are two ways parallel processors communicate, shared memory ormessage passing. Shared memory processing needs additional locking forthe data and imposes the overhead of additional processor and bus cyclesand also serializes some portion of the algorithm.

Message passing processing uses high-speed data communications networksand message buffers, but this communication adds transfer overhead onthe data communications networks as well as additional memory need formessage buffers and latency in the data communications among nodes.Designs of parallel computers use specially designed data communicationslinks so that the communication overhead will be small but it is theparallel algorithm that decides the volume of the traffic.

Many data communications network architectures are used for messagepassing among nodes in parallel computers. Compute nodes may beorganized in a network as a ‘torus’ or ‘mesh,’ for example. Also,compute nodes may be organized in a network as a tree. A torus networkconnects the nodes in a three-dimensional mesh with wrap around links.Every node is connected to its six neighbors through this torus network,and each node is addressed by its x,y,z coordinate in the mesh. In atree network, the nodes typically are connected into a binary tree: eachnode has a parent and two children (although some nodes may only havezero children or one child, depending on the hardware configuration). Incomputers that use a torus and a tree network, the two networkstypically are implemented independently of one another, with separaterouting circuits, separate physical links, and separate message buffers.

A torus network lends itself to point to point operations, but a treenetwork typically is inefficient in point to point communication. A treenetwork, however, does provide high bandwidth and low latency forcertain collective operations, message passing operations where allcompute nodes participate simultaneously, such as, for example, anallgather.

There is at this time a general trend in computer processor developmentto move from multi-core to many-core processors: from dual-, tri-,quad-, hexa-, octo-core chips to ones with tens or even hundreds ofcores. In addition, multi-core chips mixed with simultaneousmultithreading, memory-on-chip, and special-purpose heterogeneous corespromise further performance and efficiency gains, especially inprocessing multimedia, recognition and networking applications. Thistrend is impacting the supercomputing world as well, where largetransistor count chips are more efficiently used by replicating cores,rather than building chips that are very fast but very inefficient interms of power utilization.

At the same time, the network link speed and number of links into andout of a compute node are dramatically increasing. IBM's BlueGene/Q™supercomputer, for example, will have a five-dimensional torus network,which implements ten bidirectional data communications links per computenode—and BlueGene/Q will support many thousands of compute nodes. Tokeep these links filled with data, DMA engines are employed, butincreasingly, the HPC community is interested in latency. In traditionalsupercomputers with pared-down operating systems, there is little or nomulti-tasking within compute nodes. When a data communications link isunavailable, a task typically blocks or ‘spins’ on a data transmission,in effect, idling a processor until a data transmission resource becomesavailable. In the trend for more powerful individual processors, suchblocking or spinning has a bad effect on latency.

SUMMARY OF THE INVENTION

Methods, parallel computers, and computer program products for datacommunications in a parallel active messaging interface (‘PAMI’) of aparallel computer, the parallel computer including a plurality ofcompute nodes that execute a parallel application, the PAMI composed ofdata communications endpoints, each endpoint comprising a specificationof data communications parameters for a thread of execution on a computenode, including specifications of a client, a context, and a task, thecompute nodes and the endpoints coupled for data communications throughthe PAMI and through data communications resources, including receivingin an origin endpoint of the PAMI a SEND instruction, the SENDinstruction specifying a transmission of transfer data from the originendpoint to a first target endpoint; transmitting from the originendpoint to the first target endpoint a Request-To-Send (‘RTS’) messageadvising the first target endpoint of the location and size of thetransfer data; assigning by the first target endpoint to each of aplurality of target endpoints separate portions of the transfer data;and receiving by the plurality of target endpoints the transfer data.

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescriptions of example embodiments of the invention as illustrated inthe accompanying drawings wherein like reference numbers generallyrepresent like parts of example embodiments of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 sets forth a block and network diagram of an example parallelcomputer that processes data communications in a parallel activemessaging interface (‘PAMI’) according to embodiments of the presentinvention.

FIG. 2 sets forth a block diagram of an example compute node for use inparallel computers that process data communications in a PAMI accordingto embodiments of the present invention.

FIG. 3A illustrates an example Point To Point Adapter for use inparallel computers that process data communications in a PAMI accordingto embodiments of the present invention.

FIG. 3B illustrates an example Collective Operations Adapter for use inparallel computers that process data communications in a PAMI accordingto embodiments of the present invention.

FIG. 4 illustrates an example data communications network optimized forpoint to point operations for use in parallel computers that processdata communications in a PAMI according to embodiments of the presentinvention.

FIG. 5 illustrates an example data communications network optimized forcollective operations by organizing compute nodes in a tree for use inparallel computers that process data communications in a PAMI accordingto embodiments of the present invention.

FIG. 6 sets forth a block diagram of an example protocol stack for usein parallel computers that process data communications in a PAMIaccording to embodiments of the present invention.

FIG. 7 sets forth a functional block diagram of an example PAMI for usein parallel computers that process data communications in a PAMIaccording to embodiments of the present invention.

FIG. 8A sets forth a functional block diagram of example datacommunications resources for use in parallel computers that process datacommunications in a PAMI according to embodiments of the presentinvention.

FIG. 8B sets forth a functional block diagram of an example DMAcontroller—in an architecture where the DMA controller is the only DMAcontroller on a compute node—and an origin endpoint and its targetendpoint are both located on the same compute node.

FIG. 9 sets forth a functional block diagram of an example PAMI for usein parallel computers that process data communications in a PAMIaccording to embodiments of the present invention.

FIG. 10 sets forth a functional block diagram of example endpoints foruse in parallel computers that process data communications in a PAMIaccording to embodiments of the present invention.

FIG. 11 sets forth a flow chart illustrating an example method of datacommunications in a PAMI of a parallel computer according to embodimentsof the present invention.

FIG. 12 sets forth a calling sequence diagram further illustrating theoperations of the method of FIG. 11, an example method of datacommunications in a PAMI of a parallel computer according to embodimentsof the present invention.

FIG. 13 sets forth a flow chart illustrating a further example method ofdata communications in a PAMI of a parallel computer according toembodiments of the present invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS

Example methods, computers, and computer program products for datacommunications in a parallel active messaging interface (‘PAMI’) of aparallel computer according to embodiments of the present invention aredescribed with reference to the accompanying drawings, beginning withFIG. 1. FIG. 1 sets forth a block and network diagram of an exampleparallel computer (100) that processes data communications in a PAMIaccording to embodiments of the present invention. The parallel computer(100) in the example of FIG. 1 is coupled to non-volatile memory for thecomputer in the form of data storage device (118), an output device forthe computer in the form of printer (120), and an input/output devicefor the computer in the form of computer terminal (122). The parallelcomputer (100) in the example of FIG. 1 includes a plurality of computenodes (102).

The parallel computer (100) in the example of FIG. 1 includes aplurality of compute nodes (102). The compute nodes (102) are coupledfor data communications by several independent data communicationsnetworks including a high speed Ethernet network (174), a Joint TestAction Group (‘JTAG’) network (104), a tree network (106) which isoptimized for collective operations, and a torus network (108) which isoptimized point to point operations. Tree network (106) is a datacommunications network that includes data communications links connectedto the compute nodes so as to organize the compute nodes as a tree. Eachdata communications network is implemented with data communicationslinks among the compute nodes (102). The data communications linksprovide data communications for parallel operations among the computenodes of the parallel computer.

In addition, the compute nodes (102) of parallel computer are organizedinto at least one operational group (132) of compute nodes forcollective parallel operations on parallel computer (100). Anoperational group of compute nodes is the set of compute nodes uponwhich a collective parallel operation executes. Collective operationsare implemented with data communications among the compute nodes of anoperational group. Collective operations are those functions thatinvolve all the compute nodes of an operational group. A collectiveoperation is an operation, a message-passing computer programinstruction that is executed simultaneously, that is, at approximatelythe same time, by all the compute nodes in an operational group ofcompute nodes. Such an operational group may include all the computenodes in a parallel computer (100) or a subset all the compute nodes.Collective operations are often built around point to point operations.A collective operation requires that all processes on all compute nodeswithin an operational group call the same collective operation withmatching arguments. A ‘broadcast’ is an example of a collectiveoperations for moving data among compute nodes of an operational group.A ‘reduce’ operation is an example of a collective operation thatexecutes arithmetic or logical functions on data distributed among thecompute nodes of an operational group. An operational group may beimplemented as, for example, an MPI ‘communicator.’

‘MPI’ refers to ‘Message Passing Interface,’ a prior art applicationsmessaging module or parallel communications library, anapplication-level messaging module of computer program instructions fordata communications on parallel computers. Such an application messagingmodule is disposed in an application messaging layer in a datacommunications protocol stack. Examples of prior-art parallelcommunications libraries that may be improved for use with parallelcomputers that process data communications in a PAMI of a parallelcomputer according to embodiments of the present invention include IBM'sMPI library, the ‘Parallel Virtual Machine’ (‘PVM’) library, MPICH,OpenMPI, and LAM/MPI. MPI is promulgated by the MPI Forum, an open groupwith representatives from many organizations that define and maintainthe MPI standard. MPI at the time of this writing is a de facto standardfor communication among compute nodes running a parallel program on adistributed memory parallel computer. This specification sometimes usesMPI terminology for ease of explanation, although the use of MPI as suchis not a requirement or limitation of the present invention.

Most collective operations are variations or combinations of four basicoperations: broadcast, gather, scatter, and reduce. In a broadcastoperation, all processes specify the same root process, whose buffercontents will be sent. Processes other than the root specify receivebuffers. After the operation, all buffers contain the message from theroot process.

A scatter operation, like the broadcast operation, is also a one-to-manycollective operation. All processes specify the same receive count. Thesend arguments are only significant to the root process, whose bufferactually contains sendcount * N elements of a given datatype, where N isthe number of processes in the given group of compute nodes. The sendbuffer will be divided equally and dispersed to all processes (includingitself). Each compute node is assigned a sequential identifier termed a‘rank.’ After the operation, the root has sent sendcount data elementsto each process in increasing rank order. Rank 0 receives the firstsendcount data elements from the send buffer. Rank 1 receives the secondsendcount data elements from the send buffer, and so on.

A gather operation is a many-to-one collective operation that is acomplete reverse of the description of the scatter operation. That is, agather is a many-to-one collective operation in which elements of adatatype are gathered from the ranked compute nodes into a receivebuffer in a root node.

A reduce operation is also a many-to-one collective operation thatincludes an arithmetic or logical function performed on two dataelements. All processes specify the same ‘count’ and the same arithmeticor logical function. After the reduction, all processes have sent countdata elements from computer node send buffers to the root process. In areduction operation, data elements from corresponding send bufferlocations are combined pair-wise by arithmetic or logical operations toyield a single corresponding element in the root process's receivebuffer. Application specific reduction operations can be defined atruntime. Parallel communications libraries may support predefinedoperations. MPI, for example, provides the following pre-definedreduction operations:

MPI_MAX maximum MPI_MIN minimum MPI_SUM sum MPI_PROD product MPI_LANDlogical and MPI_BAND bitwise and MPI_LOR logical or MPI_BOR bitwise orMPI_LXOR logical exclusive or MPI_BXOR bitwise exclusive or

In addition to compute nodes, the example parallel computer (100)includes input/output (‘I/O’) nodes (110, 114) coupled to compute nodes(102) through one of the data communications networks (174). The I/Onodes (110, 114) provide I/O services between compute nodes (102) andI/O devices (118, 120, 122). I/O nodes (110, 114) are connected for datacommunications I/O devices (118, 120, 122) through local area network(‘LAN’) (130). Computer (100) also includes a service node (116) coupledto the compute nodes through one of the networks (104). Service node(116) provides service common to pluralities of compute nodes, loadingprograms into the compute nodes, starting program execution on thecompute nodes, retrieving results of program operations on the computernodes, and so on. Service node (116) runs a service application (124)and communicates with users (128) through a service applicationinterface (126) that runs on computer terminal (122).

As the term is used here, a parallel active messaging interface or‘PAMI’ (218) is a system-level messaging layer in a protocol stack of aparallel computer that is composed of data communications endpoints eachof which is specified with data communications parameters for a threadof execution on a compute node of the parallel computer. The PAMI is a‘parallel’ interface in that many instances of the PAMI operate inparallel on the compute nodes of a parallel computer. The PAMI is an‘active messaging interface’ in that data communications messages in thePAMI are active messages, ‘active’ in the sense that such messagesimplement callback functions to advise of message dispatch andinstruction completion and so on, thereby reducing the quantity ofacknowledgment traffic, and the like, burdening the data communicationresources of the PAMI.

Each data communications endpoint of a PAMI is implemented as acombination of a client, a context, and a task. A ‘client’ as the termis used in PAMI operations is a collection of data communicationsresources dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. A ‘context’ as the term is used in PAMIoperations is composed of a subset of a client's collection of dataprocessing resources, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution. In atleast some embodiments, the context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context. A‘task’ as the term is used in PAMI operations refers to a canonicalentity, an integer or objection oriented programming object, thatrepresents in a PAMI a process of execution of the parallel application.That is, a task is typically implemented as an identifier of aparticular instance of an application executing on a compute node, acompute core on a compute node, or a thread of execution on amulti-threading compute core on a compute node.

In the example of FIG. 1, the compute nodes (102), as well as PAMIendpoints on the compute nodes, are coupled for data communicationsthrough the PAMI (218) and through data communications resources such assource buffer (546), target buffer (548), collective network (106), andpoint-to-point network (108). The processing of data communications bythe parallel computer of FIG. 1 is further explained with reference toan origin endpoint (352), a first target endpoint (354), a plurality oftarget endpoints (560, 562) that provide additional support for datacommunications as described herein. The parallel computer of FIG. 1operates generally to process data communications in a PAMI of aparallel computer according to embodiments of the present invention byreceiving in an origin endpoint (352) of the PAMI a send instruction,the send instruction specifying a transmission of transfer data from asource buffer (546) of the origin endpoint (352) to a first targetendpoint (354); transmitting from the origin endpoint (352) to the firsttarget endpoint (354) a Request-To-Send (‘RTS’) message advising thefirst target endpoint of the location and size of the transfer data;assigning by the first target endpoint (354) to each of a plurality oftarget endpoints (560, 562) separate portions of the transfer data; andreceiving by the plurality of target endpoints (560, 562) the transferdata, copying or moving the transfer data from the source buffer (546)to a target buffer (548) of the first target endpoint (354).

In addition to the send instruction mentioned above, which readers willrecognize as a rendezvous send, data communications instructionsprocessed by the parallel computer here include both eager sendinstructions, receive instructions, DMA PUT instructions, DMA GETinstructions, and so on. Some data communications instructions,typically GETs and PUTs are one-sided DMA instructions in that there isno cooperation required from a target processor, no computation on thetarget side to complete such a PUT or GET because data is transferreddirectly to or from memory on the other side of the transfer. In thissetting, the term ‘target’ is used for either PUT or GET. A PUT targetreceives data directly into its RAM from an origin endpoint. A GETtarget provides data directly from its RAM to the origin endpoint. Thusreaders will recognize that the designation of an endpoint as an originendpoint for a transfer is a designation of the endpoint that initiatesexecution of a DMA transfer instruction—rather than a designation of thedirection of the transfer: PUT instructions transfer data from an originendpoint to a target endpoint. GET instructions transfer data from atarget endpoint to an origin endpoint.

The origin endpoint and the target endpoint, or first target endpoint intransfers that use pluralities of target endpoints, can be any twoendpoints on any of the compute nodes (102), including two endpoints onthe same compute node. A sequence of data communications instructionsresides in a work queue of a context and results in data transfersbetween two endpoints, an origin endpoint and a target endpoint—althoughas seen here, a target endpoint can function as a first target endpointamong a plurality of target endpoints for a data transfer. Datacommunications instructions are ‘active’ in the sense that theinstructions implement callback functions to advise of instructiondispatch and instruction completion, thereby reducing the quantity ofacknowledgment traffic required on the network. Each such instructioneffects a data transfer, from an origin endpoint to a target endpoint,through some form of data communications resources, networks, sharedmemory segments, adapters, DMA controllers, and the like.

The arrangement of compute nodes, networks, and I/O devices making upthe example parallel computer illustrated in FIG. 1 are for explanationonly, not for limitation of the present invention. Parallel computerscapable of data communications in a PAMI according to embodiments of thepresent invention may include additional nodes, networks, devices, andarchitectures, not shown in FIG. 1, as will occur to those of skill inthe art. For ease of explanation, the parallel computer in the exampleof FIG. 1 is illustrated with only one source buffer (546), one targetbuffer (546), one origin endpoint (352), and three target endpoints(354, 560, 562); readers will recognize, however, that practicalembodiments of such a parallel computer will include many sourcebuffers, many target buffers, many origin endpoints, and many targetendpoints. The parallel computer (100) in the example of FIG. 1 includessixteen compute nodes (102); parallel computers that process datacommunications in a PAMI according to some embodiments of the presentinvention include thousands of compute nodes. In addition to Ethernetand JTAG, networks in such data processing systems may support many datacommunications protocols including for example TCP (Transmission ControlProtocol), IP (Internet Protocol), and others as will occur to those ofskill in the art. Various embodiments of the present invention may beimplemented on a variety of hardware platforms in addition to thoseillustrated in FIG. 1.

Data communications in a PAMI according to embodiments of the presentinvention is generally implemented on a parallel computer that includesa plurality of compute nodes. In fact, such computers may includethousands of such compute nodes, with a compute node typically executingat least one instance of a parallel application. Each compute node is inturn itself a computer composed of one or more computer processors, itsown computer memory, and its own input/output (‘I/O’) adapters. Forfurther explanation, therefore, FIG. 2 sets forth a block diagram of anexample compute node (152) for use in a parallel computer that processdata communications in a PAMI according to embodiments of the presentinvention. The compute node (152) of FIG. 2 includes one or morecomputer processors (164) as well as random access memory (‘RAM’) (156).Each processor (164) can support multiple hardware compute cores (165),and each such core can in turn support multiple threads of execution,hardware threads of execution as well as software threads. Eachprocessor (164) is connected to RAM (156) through a high-speed frontside bus (161), bus adapter (194), and a high-speed memory bus (154)—andthrough bus adapter (194) and an extension bus (168) to other componentsof the compute node. Stored in RAM (156) is an application program(158), a module of computer program instructions that carries outparallel, user-level data processing using parallel algorithms.

Also stored RAM (156) is an application messaging module (216), alibrary of computer program instructions that carry outapplication-level parallel communications among compute nodes, includingpoint to point operations as well as collective operations. Although theapplication program can call PAMI routines directly, the applicationprogram (158) often executes point-to-point data communicationsoperations by calling software routines in the application messagingmodule (216), which in turn is improved according to embodiments of thepresent invention to use PAMI functions to implement suchcommunications. An application messaging module can be developed fromscratch to use a PAMI according to embodiments of the present invention,using a traditional programming language such as the C programminglanguage or C++, for example, and using traditional programming methodsto write parallel communications routines that send and receive dataamong PAMI endpoints and compute nodes through data communicationsnetworks or shared-memory transfers. In this approach, the applicationmessaging module (216) exposes a traditional interface, such as MPI, tothe application program (158) so that the application program can gainthe benefits of a PAMI with no need to recode the application. As analternative to coding from scratch, therefore, existing prior artapplication messaging modules may be improved to use the PAMI, existingmodules that already implement a traditional interface. Examples ofprior-art application messaging modules that can be improved to processdata communications in a PAMI according to embodiments of the presentinvention include such parallel communications libraries as thetraditional ‘Message Passing Interface’ (‘MPI’) library, the ‘ParallelVirtual Machine’ (‘PVM’) library, MPICH, and the like.

Also represented in RAM in the example of FIG. 2 is a PAMI (218).Readers will recognize, however, that the representation of the PAMI inRAM is a convention for ease of explanation rather than a limitation ofthe present invention, because the PAMI and its components, endpoints,clients, contexts, and so on, have particular associations with andinclusions of hardware data communications resources. In fact, the PAMIcan be implemented partly as software or firmware and hardware—or even,at least in some embodiments, entirely in hardware.

Also represented in RAM (156) in the example of FIG. 2 is a segment(227) of shared memory. In typical operation, the operating system (162)in this example compute node assigns portions of address space to eachprocessor (164), and, to the extent that the processors include multiplecompute cores (165), treats each compute core as a separate processorwith its own assignment of a portion of core memory or RAM (156) for aseparate heap, stack, memory variable storage, and so on. The defaultarchitecture for such apportionment of memory space is that eachprocessor or compute core operates its assigned portion of memoryseparately, with no ability to access memory assigned to anotherprocessor or compute core. Upon request, however, the operating systemgrants to one processor or compute core the ability to access a segmentof memory that is assigned to another processor or compute core, andsuch a segment is referred to in this specification as a ‘segment ofshared memory.’

In the example of FIG. 2, each processor or compute core has uniformaccess to the RAM (156) on the compute node, so that accessing a segmentof shared memory is equally fast regardless where the shared segment islocated in physical memory. In some embodiments, however, modules ofphysical memory are dedicated to particular processors, so that aprocessor may access local memory quickly and remote memory more slowly,a configuration referred to as a Non-Uniform Memory Access or ‘NUMA.’ Insuch embodiments, a segment of shared memory can be configured locallyfor one endpoint and remotely for another endpoint—or remotely from bothendpoints of a communication. From the perspective of an origin endpointtransmitting data through a segment of shared memory that is configuredremotely with respect to the origin endpoint, transmitting data throughthe segment of shared memory will appear slower that if the segment ofshared memory were configured locally with respect to the originendpoint—or if the segment were local to both the origin endpoint andthe target endpoint. This is the effect of the architecture representedby the compute node (152) in the example of FIG. 2 with all processorsand all compute cores coupled through the same bus to the RAM—that allaccesses to segments of memory shared among processes or processors onthe compute node are local—and therefore very fast.

Also stored in RAM (156) in the example compute node of FIG. 2 is anoperating system (162), a module of computer program instructions androutines for an application program's access to other resources of thecompute node. It is possible, in some embodiments at least, for anapplication program, an application messaging module, and a PAMI in acompute node of a parallel computer to run threads of execution with nouser login and no security issues because each such thread is entitledto complete access to all resources of the node. The quantity andcomplexity of duties to be performed by an operating system on a computenode in a parallel computer therefore can be somewhat smaller and lesscomplex than those of an operating system on a serial computer with manythreads running simultaneously with various level of authorization foraccess to resources. In addition, there is no video I/O on the computenode (152) of FIG. 2, another factor that decreases the demands on theoperating system. The operating system may therefore be quitelightweight by comparison with operating systems of general purposecomputers, a pared down or ‘lightweight’ version as it were, or anoperating system developed specifically for operations on a particularparallel computer. Operating systems that may be improved or simplifiedfor use in a compute node according to embodiments of the presentinvention include UNIX™, Linux™, Microsoft XP™, AIX™, IBM's i5/OS™, andothers as will occur to those of skill in the art.

The example compute node (152) of FIG. 2 includes several communicationsadapters (172, 176, 180, 188) for implementing data communications withother nodes of a parallel computer. Such data communications may becarried out serially through RS-232 connections, through external busessuch as USB, through data communications networks such as IP networks,and in other ways as will occur to those of skill in the art.Communications adapters implement the hardware level of datacommunications through which one computer sends data communications toanother computer, directly or through a network. Examples ofcommunications adapters for use in computers that process datacommunications in a parallel active messaging interface (‘PAMI’)according to embodiments of the present invention include modems forwired communications, Ethernet (IEEE 802.3) adapters for wired networkcommunications, and 802.11b adapters for wireless networkcommunications.

The data communications adapters in the example of FIG. 2 include aGigabit Ethernet adapter (172) that couples example compute node (152)for data communications to a Gigabit Ethernet (174). Gigabit Ethernet isa network transmission standard, defined in the IEEE 802.3 standard,that provides a data rate of 1 billion bits per second (one gigabit).Gigabit Ethernet is a variant of Ethernet that operates over multimodefiber optic cable, single mode fiber optic cable, or unshielded twistedpair.

The data communications adapters in the example of FIG. 2 includes aJTAG Slave circuit (176) that couples example compute node (152) fordata communications to a JTAG Master circuit (178). JTAG is the usualname for the IEEE 1149.1 standard entitled Standard Test Access Port andBoundary-Scan Architecture for test access ports used for testingprinted circuit boards using boundary scan. JTAG is so widely adaptedthat, at this time, boundary scan is more or less synonymous with JTAG.JTAG is used not only for printed circuit boards, but also forconducting boundary scans of integrated circuits, and is also used as amechanism for debugging embedded systems, providing a convenient “backdoor” into the system. The example compute node of FIG. 2 may be allthree of these: It typically includes one or more integrated circuitsinstalled on a printed circuit board and may be implemented as anembedded system having its own processor, its own memory, and its ownI/O capability. JTAG boundary scans through JTAG Slave (176) mayefficiently configure processor registers and memory in compute node(152) for use in data communications in a PAMI according to embodimentsof the present invention.

The data communications adapters in the example of FIG. 2 includes aPoint To Point Adapter (180) that couples example compute node (152) fordata communications to a data communications network (108) that isoptimal for point to point message passing operations such as, forexample, a network configured as a three-dimensional torus or mesh.Point To Point Adapter (180) provides data communications in sixdirections on three communications axes, x, y, and z, through sixbidirectional links: +x (181), −x (182), +y (183), −y (184), +z (185),and −z (186). For ease of explanation, the Point To Point Adapter (180)of FIG. 2 as illustrated is configured for data communications in threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in data communicationsin a PAMI of a parallel computer according to embodiments of the presentinvention may in fact be implemented so as to support communications intwo dimensions, four dimensions, five dimensions, and so on.

The data communications adapters in the example of FIG. 2 includes aCollective Operations Adapter (188) that couples example compute node(152) for data communications to a network (106) that is optimal forcollective message passing operations such as, for example, a networkconfigured as a binary tree. Collective Operations Adapter (188)provides data communications through three bidirectional links: two tochildren nodes (190) and one to a parent node (192). The example computenode (152) includes a number of arithmetic logic units (‘ALUs’). ALUs(166) are components of processors (164), and a separate ALU (170) isdedicated to the exclusive use of collective operations adapter (188)for use in performing the arithmetic and logical functions of reductionoperations. Computer program instructions of a reduction routine in anapplication messaging module (216) or a PAMI (218) may latch aninstruction for an arithmetic or logical function into instructionregister (169). When the arithmetic or logical function of a reductionoperation is a ‘sum’ or a ‘logical OR,’ for example, collectiveoperations adapter (188) may execute the arithmetic or logical operationby use of an ALU (166) in a processor (164) or, typically much faster,by use of the dedicated ALU (170).

The example compute node (152) of FIG. 2 includes a direct memory access(‘DMA’) controller (225), a module of automated computing machinery thatimplements, through communications with other DMA engines on othercompute nodes, or on a same compute node, direct memory access to andfrom memory on its own compute node as well as memory on other computenodes. Direct memory access is a way of reading and writing to and frommemory of compute nodes with reduced operational burden on computerprocessors (164); a CPU initiates a DMA transfer, but the CPU does notexecute the DMA transfer. A DMA transfer essentially copies a block ofmemory from one compute node to another, or between RAM segments ofapplications on the same compute node, from an origin to a target for aPUT operation, from a target to an origin for a GET operation.

For further explanation, FIG. 3A illustrates an example of a Point ToPoint Adapter (180) useful in parallel computers that process datacommunications in a PAMI according to embodiments of the presentinvention. Point To Point Adapter (180) is designed for use in a datacommunications network optimized for point to point operations, anetwork that organizes compute nodes in a three-dimensional torus ormesh. Point To Point Adapter (180) in the example of FIG. 3A providesdata communication along an x-axis through four unidirectional datacommunications links, to and from the next node in the −x direction(182) and to and from the next node in the +x direction (181). Point ToPoint Adapter (180) also provides data communication along a y-axisthrough four unidirectional data communications links, to and from thenext node in the −y direction (184) and to and from the next node in the+y direction (183). Point To Point Adapter (180) in also provides datacommunication along a z-axis through four unidirectional datacommunications links, to and from the next node in the −z direction(186) and to and from the next node in the +z direction (185). For easeof explanation, the Point To Point Adapter (180) of FIG. 3A asillustrated is configured for data communications in only threedimensions, x, y, and z, but readers will recognize that Point To PointAdapters optimized for point-to-point operations in a parallel computerthat processes data communications according to embodiments of thepresent invention may in fact be implemented so as to supportcommunications in two dimensions, four dimensions, five dimensions, andso on. Several supercomputers now use five dimensional mesh or torusnetworks, including, for example, IBM's Blue Gene Q™.

For further explanation, FIG. 3B illustrates an example of a CollectiveOperations Adapter (188) useful in a parallel computer that processesdata communications in a PAMI according to embodiments of the presentinvention. Collective Operations Adapter (188) is designed for use in anetwork optimized for collective operations, a network that organizescompute nodes of a parallel computer in a binary tree. CollectiveOperations Adapter (188) in the example of FIG. 3B provides datacommunication to and from two children nodes through four unidirectionaldata communications links (190). Collective Operations Adapter (188)also provides data communication to and from a parent node through twounidirectional data communications links (192).

For further explanation, FIG. 4 sets forth a line drawing illustratingan example data communications network (108) optimized forpoint-to-point operations useful in parallel computers that process datacommunications in a PAMI according to embodiments of the presentinvention. In the example of FIG. 4, dots represent compute nodes (102)of a parallel computer, and the dotted lines between the dots representdata communications links (103) between compute nodes. The datacommunications links are implemented with point-to-point datacommunications adapters similar to the one illustrated for example inFIG. 3A, with data communications links on three axis, x, y, and z, andto and fro in six directions +x (181), −x (182), +y (183), −y (184), +z(185), and −z (186). The links and compute nodes are organized by thisdata communications network optimized for point-to-point operations intoa three dimensional mesh (105). The mesh (105) has wrap-around links oneach axis that connect the outermost compute nodes in the mesh (105) onopposite sides of the mesh (105). These wrap-around links form a torus(107). Each compute node in the torus has a location in the torus thatis uniquely specified by a set of x, y, z coordinates. Readers will notethat the wrap-around links in the y and z directions have been omittedfor clarity, but are configured in a similar manner to the wrap-aroundlink illustrated in the x direction. For clarity of explanation, thedata communications network of FIG. 4 is illustrated with only 27compute nodes, but readers will recognize that a data communicationsnetwork optimized for point-to-point operations in a parallel computerthat processes data communications according to embodiments of thepresent invention may contain only a few compute nodes or may containthousands of compute nodes. For ease of explanation, the datacommunications network of FIG. 4 is illustrated with only threedimensions: x, y, and z, but readers will recognize that a datacommunications network optimized for point-to-point operations may infact be implemented in two dimensions, four dimensions, five dimensions,and so on. As mentioned, several supercomputers now use five dimensionalmesh or torus networks, including IBM's Blue Gene Q™.

For further explanation, FIG. 5 illustrates an example datacommunications network (106) optimized for collective operations byorganizing compute nodes in a tree. The example data communicationsnetwork of FIG. 5 includes data communications links connected to thecompute nodes so as to organize the compute nodes as a tree. In theexample of FIG. 5, dots represent compute nodes (102) of a parallelcomputer, and the dotted lines (103) between the dots represent datacommunications links between compute nodes. The data communicationslinks are implemented with collective operations data communicationsadapters similar to the one illustrated for example in FIG. 3B, witheach node typically providing data communications to and from twochildren nodes and data communications to and from a parent node, withsome exceptions. Nodes in a binary tree may be characterized as a rootnode (202), branch nodes (204), and leaf nodes (206). The root node(202) has two children but no parent. The leaf nodes (206) each has aparent, but leaf nodes have no children. The branch nodes (204) each hasboth a parent and two children. The links and compute nodes are therebyorganized by this data communications network optimized for collectiveoperations into a binary tree (106). For clarity of explanation, thedata communications network of FIG. 5 is illustrated with only 31compute nodes, but readers will recognize that a data communicationsnetwork optimized for collective operations for use in parallelcomputers that process data communications in a PAMI according toembodiments of the present invention may contain only a few computenodes or hundreds or thousands of compute nodes.

In the example of FIG. 5, each node in the tree is assigned a unitidentifier referred to as a ‘rank’ (250). The rank actually identifiesan instance of a parallel application that is executing on a computenode. That is, the rank is an application-level identifier. Using therank to identify a node assumes that only one such instance of anapplication is executing on each node. A compute node can, however,support multiple processors, each of which can support multipleprocessing cores—so that more than one process or instance of anapplication can easily be present under execution on any given computenode—or in all the compute nodes, for that matter. To the extent thatmore than one instance of an application executes on a single computenode, the rank identifies the instance of the application as such ratherthan the compute node. A rank uniquely identifies an application'slocation in the tree network for use in both point-to-point andcollective operations in the tree network. The ranks in this example areassigned as integers beginning with ‘0’ assigned to the root instance orroot node (202), ‘1’ assigned to the first node in the second layer ofthe tree, ‘2’ assigned to the second node in the second layer of thetree, ‘3’ assigned to the first node in the third layer of the tree, ‘4’assigned to the second node in the third layer of the tree, and so on.For ease of illustration, only the ranks of the first three layers ofthe tree are shown here, but all compute nodes, or rather allapplication instances, in the tree network are assigned a unique rank.Such rank values can also be assigned as identifiers of applicationinstances as organized in a mesh or torus network.

For further explanation, FIG. 6 sets forth a block diagram of an exampleprotocol stack useful in parallel computers that process datacommunications in a PAMI according to embodiments of the presentinvention. The example protocol stack of FIG. 6 includes a hardwarelayer (214), a system messaging layer (212), an application messaginglayer (210), and an application layer (208). For ease of explanation,the protocol layers in the example stack of FIG. 6 are shown connectingan origin compute node (222) and a target compute node (224), althoughit is worthwhile to point out that in embodiments that effect DMA datatransfers, the origin compute node and the target compute node can bethe same compute node. The granularity of connection through the systemmessaging layer (212), which is implemented with a PAMI (218), is finerthan merely compute node to compute node—because, again, communicationsamong endpoints often is communications among endpoints on the samecompute node. For further explanation, recall that the PAMI (218)connects endpoints, connections specified by combinations of clients,contexts, and tasks, each such combination being specific to a thread ofexecution on a compute node, with each compute node capable ofsupporting many threads and therefore many endpoints. Every endpointtypically can function as both an origin endpoint or a target endpointfor data transfers through a PAMI, and both the origin endpoint and itstarget endpoint can be located on the same compute node. So an origincompute node (222) and its target compute node (224) can in fact, andoften will, be the same compute node.

The application layer (208) provides communications among instances of aparallel application (158) running on the compute nodes (222, 224) byinvoking functions in an application messaging module (216) installed oneach compute node. Communications among instances of the applicationthrough messages passed between the instances of the application.Applications may communicate messages invoking function of anapplication programming interface (‘API’) exposed by the applicationmessaging module (216). In this approach, the application messagingmodule (216) exposes a traditional interface, such as an API of an MPIlibrary, to the application program (158) so that the applicationprogram can gain the benefits of a PAMI, reduced network traffic,callback functions, and so on, with no need to recode the application.Alternatively, if the parallel application is programmed to use PAMIfunctions, the application can call the PAMI functions directly, withoutgoing through the application messaging module.

The example protocol stack of FIG. 6 includes a system messaging layer(212) implemented here as a PAMI (218). The PAMI provides system-leveldata communications functions that support messaging in the applicationlayer (602) and the application messaging layer (610). Such system-levelfunctions are typically invoked through an API exposed to theapplication messaging modules (216) in the application messaging layer(210). Although developers can in fact access a PAMI API directly bycoding an application to do so, a PAMI's system-level functions in thesystem messaging layer (212) in many embodiments are isolated from theapplication layer (208) by the application messaging layer (210), makingthe application layer somewhat independent of system specific details.With an application messaging module presenting a standard MPI API to anapplication, for example, with the application messaging module retooledto use the PAMI to carry out the low-level messaging functions, theapplication gains the benefits of a PAMI with no need to incur theexpense of reprogramming the application to call the PAMI directly.Because, however, some applications will in fact be reprogrammed to callthe PAMI directly, all entities in the protocol stack above the PAMI areviewed by PAMI as applications. When PAMI functions are invoked byentities above the PAMI in the stack, the PAMI makes no distinctionwhether the caller is in the application layer or the applicationmessaging layer, no distinction whether the caller is an application assuch or an MPI library function invoked by an application. As far as thePAMI is concerned, any caller of a PAMI function is an application.

The protocol stack of FIG. 6 includes a hardware layer (634) thatdefines the physical implementation and the electrical implementation ofaspects of the hardware on the compute nodes such as the bus, networkcabling, connector types, physical data rates, data transmissionencoding and many other factors for communications between the computenodes (222) on the physical network medium. In parallel computers thatprocess data communications with DMA controllers according toembodiments of the present invention, the hardware layer includes DMAcontrollers and network links, including routers, packet switches, andthe like.

For further explanation, FIG. 7 sets forth a functional block diagram ofan example PAMI (218) for use in parallel computers that process datacommunications in a PAMI according to embodiments of the presentinvention. The PAMI (218) provides an active messaging layer thatsupports both point to point communications in a mesh or torus as wellas collective operations, gathers, reductions, barriers, and the like intree networks, for example. The PAMI is a multithreaded parallelcommunications engine designed to provide low level message passingfunctions, many of which are one-sided, and abstract such functions forhigher level messaging middleware, referred to in this specification as‘application messaging modules’ in an application messaging layer. Inthe example of FIG. 7, the application messaging layer is represented bya generic MPI module (258), appropriate for ease of explanation becausesome form of MPI is a de facto standard for such messaging middleware.Compute nodes and communications endpoints of a parallel computer (102on FIG. 1) are coupled for data communications through such a PAMI andthrough data communications resources (294, 296, 314) that include DMAcontrollers, network adapters, and data communications networks throughwhich controllers and adapters deliver data communications. The PAMI(218) provides data communications among data communications endpoints,where each endpoint is specified by data communications parameters for athread of execution on a compute node, including specifications of aclient, a context, and a task.

The PAMI (218) in this example includes PAMI clients (302, 304), tasks(286, 298), contexts (190, 292, 310, 312), and endpoints (288, 300). APAMI client is a collection of data communications resources (294, 295,314) dedicated to the exclusive use of an application-level dataprocessing entity, an application or an application messaging modulesuch as an MPI library. Data communications resources assigned incollections to PAMI clients are explained in more detail below withreference to FIGS. 8A and 8B. PAMI clients (203, 304 on FIG. 7) enablehigher level middleware, application messaging modules, MPI libraries,and the like, to be developed independently so that each can be usedconcurrently by an application. Although the application messaging layerin FIG. 7 is represented for example by a single generic MPI module(258), in fact, a PAMI, operating multiple clients, can support multiplemessage passing libraries or application messaging modulessimultaneously, a fact that is explained in more detail with referenceto FIG. 9. FIG. 9 sets forth a functional block diagram of an examplePAMI (218) useful in parallel computers that process data communicationsin a PAMI according to embodiments of the present invention in which theexample PAMI operates, on behalf of an application (158), with multipleapplication messaging modules (502-510) simultaneously. The application(158) can have multiple messages in transit simultaneously through eachof the application messaging modules (502-510). Each context (512-520)carries out, through post and advance functions, data communications forthe application on data communications resources in the exclusivepossession, in each client, of that context. Each context carries outdata communications operations independently and in parallel with othercontexts in the same or other clients. In the example FIG. 9, eachclient (532-540) includes a collection of data communications resources(522-530) dedicated to the exclusive use of an application-level dataprocessing entity, one of the application messaging modules (502-510):

-   -   IBM MPI Library (502) operates through context (512) data        communications resources (522) dedicated to the use of PAMI        client (532),    -   MPICH Library (504) operates through context (514) data        communications resources (524) dedicated to the use of PAMI        client (534),    -   Unified Parallel C (‘UPC’) Library (506) operates through        context (516) data communications resources (526) dedicated to        the use of PAMI client (536),    -   Partitioned Global Access Space (‘PGAS’) Runtime Library (508)        operates through context (518) data communications resources        (528) dedicated to the use of PAMI client (538), and    -   Aggregate Remote Memory Copy Interface (‘ARMCI’) Library (510)        operates through context (520) data communications resources        (530) dedicated to the use of PAMI client (540).

Again referring to the example of FIG. 7: The PAMI (218) includes tasks,listed in task lists (286, 298) and identified (250) to the application(158). A ‘task’ as the term is used in PAMI operations is aplatform-defined integer datatype that identifies a canonicalapplication process, an instance of a parallel application (158). Verycarefully in this specification, the term ‘task’ is always used to referonly to this PAMI structure, not the traditional use of the computerterm ‘task’ to refer to a process or thread of execution. In thisspecification, the term ‘process’ refers to a canonical data processingprocess, a container for threads in a multithreading environment. Inparticular in the example of FIG. 7, the application (158) isimplemented as a canonical process with multiple threads (251-254)assigned various duties by a leading thread (251) which itself executesan instance of a parallel application program. Each instance of aparallel application is assigned a task; each task so assigned can be aninteger value, for example, in a C environment, or a separate taskobject in a C++ or Java environment. The tasks are components ofcommunications endpoints, but are not themselves communicationsendpoints; tasks are not addressed directly for data communications inPAMI. This gives a finer grained control than was available in priormessage passing art. Each client has its own list (286, 298) of tasksfor which its contexts provide services; this allows each process topotentially reside simultaneously in two or more differentcommunications domains as will be the case in certain advanced computersusing, for example, one type of processor and network in one domain anda completely different processor type and network in another domain, allin the same computer.

The PAMI (218) includes contexts (290, 292, 310, 312). A ‘context’ asthe term is used in PAMI operations is composed of a subset of aclient's collection of data processing resources, context functions, anda work queue of data transfer instructions to be performed by use of thesubset through the context functions operated by an assigned thread ofexecution. That is, a context represents a partition of the local datacommunications resources assigned to a PAMI client. Every context withina client has equivalent functionality and semantics. Context functionsimplement contexts as threading points that applications use to optimizeconcurrent communications. Communications initiated by a local process,an instance of a parallel application, uses a context object to identifythe specific threading point that will be used to issue a particularcommunication independent of communications occurring in other contexts.In the example of FIG. 7, where the application (158) and theapplication messaging module (258) are both implemented as canonicalprocesses with multiple threads of execution, each has assigned ormapped particular threads (253, 254, 262, 264) to advance (268, 270,276, 278) work on the contexts (290, 292, 310, 312), including executionof local callbacks (272, 280). In particular, the local event callbackfunctions (272, 280) associated with any particular communication areinvoked by the thread advancing the context that was used to initiatethe communication operation in the first place. Like PAMI tasks,contexts are not used to directly address a communication destination ortarget, as they are a local resource.

Context functions, explained here with regard to references (472-482) onFIG. 9, include functions to create (472) and destroy (474) contexts,functions to lock (476) and unlock (478) access to a context, andfunctions to post (480) and advance (480) work in a context. For ease ofexplanation, the context functions (472-482) are illustrated in only oneexpanded context (512); readers will understand, however, that all PAMIcontexts have similar context functions. The create (472) and destroy(474) functions are, in an object-oriented sense, constructors anddestructors. In the example embodiments described in thisspecifications, post (480) and advance (482) functions on a context arecritical sections, not thread safe. Applications using suchnon-reentrant functions must somehow ensure that critical sections areprotected from re-entrant use. Applications can use mutual exclusionlocks to protect critical sections. The lock (476) and unlock (478)functions in the example of FIG. 9 provide and operate such a mutualexclusion lock to protect the critical sections in the post (480) andadvance (482) functions. If only a single thread posts or advances workon a context, then that thread need never lock that context. To theextent that progress is driven independently on a context by a singlethread of execution, then no mutual exclusion locking of the contextitself is required—provided that no other thread ever attempts to call afunction on such a context. If more than one thread will post or advancework on a context, each such thread must secure a lock before calling apost or an advance function on that context. This is one reason why itis probably a preferred architecture, given sufficient resources, toassign one thread to operate each context. Progress can be driven withadvance (482) functions concurrently among multiple contexts by usingmultiple threads, as desired by an application—shown in the example ofFIG. 7 by threads (253, 254, 262, 264) which advance work concurrently,independently and in parallel, on contexts (290, 292, 310, 312).

Posts and advances (480, 482 on FIG. 9) are functions called on acontext, either in a C-type function with a context ID as a parameter,or in object oriented practice where the calling entity possesses areference to a context or a context object as such and the posts andadvances are member methods of a context object. Again referring to FIG.7: Application-level entities, application programs (158) andapplication messaging modules (258), post (266, 274) data communicationsinstructions, including SENDs, RECEIVEs, PUTs, GETs, and so on, to thework queues (282, 284, 306, 308) in contexts and then call advancefunctions (268, 270, 276, 278) on the contexts to progress specific dataprocessing and data communications that carry out the instructions. Thedata processing and data communications effected by the advancefunctions include specific messages, request to send (‘RTS’) messages,acknowledgments, callback execution, transfers of transfer data orpayload data, and so on. Advance functions therefore operate generallyby checking a work queue for any new instructions that need to beinitiated and checking data communications resources for any incomingmessage traffic that needs to be administered as well as increases instorage space available for outgoing message traffic, with callbacks andthe like. Advance functions also carry out or trigger transfers oftransfer data or payload data.

In at least some embodiments, a context's subset of a client's dataprocessing resources is dedicated to the exclusive use of the context.In the example of FIG. 7, context (290) has a subset (294) of a client's(302) data processing resources dedicated to the exclusive use of thecontext (290), and context (292) has a subset (296) of a client's (302)data processing resources dedicated to the exclusive use of the context(292). Advance functions (268, 270) called on contexts (290, 292)therefore never need to secure a lock on a data communications resourcebefore progressing work on a context—because each context (290, 292) hasexclusive use of dedicated data communications resources. Usage of datacommunications resources in this example PAMI (218), however, is notthread-safe. When data communications resources are shared amongcontexts, mutual exclusion locks are needed. In contrast to theexclusive usage of resources by contexts (290, 292), contexts (310, 312)share access to their client's data communications resource (314) andtherefore do not have data communications resources dedicated toexclusive use of a single context. Contexts (310, 312) therefore alwaysmust secure a mutual exclusion lock on a data communications resourcebefore using the resource to send or receive administrative messages ortransfer data.

For further explanation, here is an example pseudocode Hello Worldprogram for an application using a PAMI:

  int main(int argc, char ** argv) {  PAMI_client_t client; PAMI_context_t context;  PAMI_result_t status = PAMI_ERROR;  constchar *name = “PAMI”;  status = PAMI_Client_initialize(name, &client); size_t_n = 1;  status = PAMI_Context_createv(client, NULL, 0, &context,_n);  PAMI_configuration _t configuration;  configuration.name =PAMI_TASK_ID;  status = PAMI_Configuration_query(client,&configuration);  size_t task_id = configuration.value.intval; configuration.name = PAMI_NUM_TASKS;  status =PAMI_Configuration_query(client, &configuration);  size_t num_tasks =configuration.value.intval;  fprintf (stderr, “Hello process %d of%d\n”, task_id, num_tasks);  status = PAMI_Context_destroy(context); status = PAMI_Client_finalize(client);  return 0; }

This short program is termed ‘pseudocode’ because it is an explanationin the form of computer code, not a working model, not an actual programfor execution. In this pseudocode example, an application initializes aclient and a context for an application named “PAMI.”PAMI_Client_initialize and PAMI_Context_createv are initializationfunctions (316) exposed to applications as part of a PAMI's API. Thesefunctions, in dependence upon the application name “PAMI,” pull from aPAMI configuration (318) the information needed to establish a clientand a context for the application. The application uses this segment:

-   -   PAMI_configuration_t configuration;    -   configuration.name=PAMI_TASK_ID;    -   status=PAMI_Configuration_query(client, &configuration);    -   size_t task_id=configuration.value.intval;        to retrieve its task ID and this segment:    -   configuration.name=PAMI_NUM_TASKS;    -   status=PAMI_Configuration_query(client, &configuration);    -   size_t num_tasks=configuration.value.intval;        to retrieve the number of tasks presently configured to carry        out parallel communications and process data communications        event in the PAMI. The applications prints “Hello process        task_id of num_tasks,” where task_id is the task ID of the        subject instance of a parallel application, and num_tasks is the        number of instances of the application executing in parallel on        compute nodes. Finally, the application destroys the context and        terminates the client.

For further explanation of data communications resources assigned incollections to PAMI clients, FIG. 8A sets forth a block diagram ofexample data communications resources (220) useful in parallel computersthat process data communications in a PAMI according to embodiments ofthe present invention. The data communications resources of FIG. 8Ainclude a gigabit Ethernet adapter (238), an Infiniband adapter (240), aFibre Channel adapter (242), a PCI Express adapter (246), a collectiveoperations network configured as a tree (106), shared memory (227), DMAcontrollers (225, 226), and a network (108) configured as apoint-to-point torus or mesh like the network described above withreference to FIG. 4. A PAMI is configured with clients, each of which isin turn configured with certain collections of such data communicationsresources—so that, for example, the PAMI client (302) in the PAMI (218)in the example of FIG. 7 can have dedicated to its use a collection ofdata communications resources composed of six segments (227) of sharedmemory, six Gigabit Ethernet adapters (238), and six Infiniband adapters(240). And the PAMI client (304) can have dedicated to its use six FibreChannel adapters (242), a DMA controller (225), a torus network (108),and five segments (227) of shared memory. And so on.

The DMA controllers (225, 226) in the example of FIG. 8A each isconfigured with DMA control logic in the form of a DMA engine (228,229), an injection FIFO buffer (230), and a receive FIFO buffer (232).The DMA engines (228, 229) can be implemented as hardware components,logic networks of a DMA controller, in firmware, as software operatingan embedded controller, as various combinations of software, firmware,or hardware, and so on. Each DMA engine (228, 229) operates on behalf ofendpoints to send and receive DMA transfer data through the network(108). The DMA engines (228, 229) operate the injection buffers (230,232) by processing first-in-first-out descriptors (234, 236) in thebuffers, hence the designation ‘injection FIFO’ and ‘receive FIFO.’

For further explanation, here is an example use case, a description ofthe overall operation of an example PUT DMA transfer using the DMAcontrollers (225, 226) and network (108) in the example of FIG. 8A: Anoriginating application (158), which is typically one instance of aparallel application running on a compute node, places a quantity oftransfer data (494) at a location in its RAM (155). The application(158) then calls a post function (480) on a context (512) of an originendpoint (352), posting a PUT instruction (390) into a work queue (282)of the context (512); the PUT instruction (390) specifies a targetendpoint (354) to which the transfer data is to be sent as well assource and destination memory locations. The application then calls anadvance function (482) on the context (512). The advance function (482)finds the new PUT instruction in its work queue (282) and inserts a datadescriptor (234) into the injection FIFO of the origin DMA controller(225); the data descriptor includes the source and destination memorylocations and the specification of the target endpoint. The origin DMAengine (225) then transfers the data descriptor (234) as well as thetransfer data (494) through the network (108) to the DMA controller(226) on the target side of the transaction. The target DMA engine(229), upon receiving the data descriptor and the transfer data, placesthe transfer data (494) into the RAM (156) of the target application atthe location specified in the data descriptor and inserts into thetarget DMA controller's receive FIFO (232) a data descriptor (236) thatspecifies the target endpoint and the location of the transfer data(494) in RAM (156). The target application (159) or application instancecalls an advance function (483) on a context (513) of the targetendpoint (354). The advance function (483) checks the communicationsresources assigned to its context (513) for incoming messages, includingchecking the receive FIFO (232) of the target DMA controller (226) fordata descriptors that specify the target endpoint (354). The advancefunction (483) finds the data descriptor for the PUT transfer andadvises the target application (159) that its transfer data has arrived.A GET-type DMA transfer works in a similar manner, with some differencesdescribed in more detail below, including, of course, the fact thattransfer data flows in the opposite direction. Similarly, typical SENDtransfers also operate similarly, some with rendezvous protocols, somewith eager protocols, with data transmitted in packets over the anetwork through non-DMA network adapters rather than DMA controllers.

The example of FIG. 8A includes two DMA controllers (225, 226). DMAtransfers between endpoints on separate compute nodes use two DMAcontrollers, one on each compute node. Compute nodes can be implementedwith multiple DMA controllers so that many or even all DMA transferseven among endpoints on a same compute node can be carried out using twoDMA engines. In some embodiments at least, however, a compute node, likethe example compute node (152) of FIG. 2, has only one DMA engine, sothat that DMA engine can be use to conduct both sides of transfersbetween endpoints on that compute node. For further explanation of thisfact, FIG. 8B sets forth a functional block diagram of an example DMAcontroller (225) operatively coupled to a network (108)—in anarchitecture where this DMA controller (225) is the only DMA controlleron a compute node—and an origin endpoint (352) and its target endpoint(354) are both located on the same compute node (152). In the example ofFIG. 8B, a single DMA engine (228) operates with two threads ofexecution (502, 504) on behalf of endpoints (352, 354) on a same computenode to send and receive DMA transfer data through a segment (227) ofshared memory. A transmit thread (502) injects transfer data into thenetwork (108) as specified in data descriptors (234) in an injectionFIFO buffer (230), and a receive thread (502) receives transfer datafrom the network (108) as specified in data descriptors (236) in areceive FIFO buffer (232).

The overall operation of an example PUT DMA transfer with the DMAcontrollers (225) and the network (108) in the example of FIG. 8B is: Anoriginating application (158), that is actually one of multipleinstances (158, 159) of a parallel application running on a compute node(152) in separate threads of execution, places a quantity of transferdata (494) at a location in its RAM (155). The application (158) thencalls a post function (480) on a context (512) of an origin endpoint(352), posting a PUT instruction (390) into a work queue (282) of thecontext (512); the PUT instruction specifies a target endpoint (354) towhich the transfer data is to be sent as well as source and destinationmemory locations. The application (158) then calls an advance function(482) on the context (512). The advance function (482) finds the new PUTinstruction (390) in its work queue (282) and inserts a data descriptor(234) into the injection FIFO of the DMA controller (225); the datadescriptor includes the source and destination memory locations and thespecification of the target endpoint.

The DMA engine (225) then transfers by its transmit and receive threads(502, 504) through the network (108) the data descriptor (234) as wellas the transfer data (494). The DMA engine (228), upon receiving by itsreceive thread (504) the data descriptor and the transfer data, placesthe transfer data (494) into the RAM (156) of the target application andinserts into the DMA controller's receive FIFO (232) a data descriptor(236) that specifies the target endpoint and the location of thetransfer data (494) in RAM (156). The target application (159) calls anadvance function (483) on a context (513) of the target endpoint (354).The advance function (483) checks the communications resources assignedto its context for incoming messages, including checking the receiveFIFO (232) of the DMA controller (225) for data descriptors that specifythe target endpoint (354). The advance function (483) finds the datadescriptor for the PUT transfer and advises the target application (159)that its transfer data has arrived. Again, a GET-type DMA transfer worksin a similar manner, with some differences described in more detailbelow, including, of course, the fact that transfer data flows in theopposite direction. And typical SEND transfers also operate similarly,some with rendezvous protocols, some with eager protocols, with datatransmitted in packets over the a network through non-DMA networkadapters rather than DMA controllers.

By use of an architecture like that illustrated and described withreference to FIG. 8B, a parallel application or an application messagingmodule that is already programmed to use DMA transfers can gain thebenefit of the speed of DMA data transfers among endpoints on the samecompute node with no need to reprogram the applications or theapplication messaging modules to use the network in other modes. In thisway, an application or an application messaging module, alreadyprogrammed for DMA, can use the same DMA calls through a same API forDMA regardless whether subject endpoints are on the same compute node oron separate compute nodes.

For further explanation, FIG. 10 sets forth a functional block diagramof example endpoints useful in parallel computers that process datacommunications in a PAMI according to embodiments of the presentinvention. In the example of FIG. 10, a PAMI (218) is implemented withinstances on two separate compute nodes (152, 153) that include fourendpoints (338, 340, 342, 344). These endpoints are opaque objects usedto address an origin or destination in a process and are constructedfrom a (client, task, context) tuple. Non-DMA SEND and RECEIVEinstructions as well as DMA instructions such as PUT and GET address adestination by use of an endpoint object or endpoint identifier.

Each endpoint (338, 340, 342, 344) in the example of FIG. 10 is composedof a client (302, 303, 304, 305), a task (332, 333, 334, 335), and acontext (290, 292, 310, 312). Using a client a component in thespecification of an endpoint disambiguates the task and contextidentifiers, as these identifiers may be the same for multiple clients.A task is used as a component in the specification of an endpoint toconstruct an endpoint to address a process accessible through a context.A context in the specification of an endpoint identifies, refers to, orrepresents the specific context associated with a the destination ortarget task—because the context identifies a specific threading point ona task. A context offset identifies which threading point is to processa particular communications operation. Endpoints enable “crosstalk”which is the act of issuing communication on a local context with aparticular context offset that is directed to a destination endpointwith no correspondence to a source context or source context offset.

For efficient utilization of storage in an environment where multipletasks of a client reside on the same physical compute node, anapplication may choose to write an endpoint table (288, 300 on FIG. 7)in a segment of shared memory (227, 346, 348). It is the responsibilityof the application to allocate such segments of shared memory andcoordinate the initialization and access of any data structures sharedbetween processes. This includes any endpoint objects which are createdby one process or instance of an application and read by anotherprocess.

Endpoints (342, 344) on compute node (153) serve respectively twoapplication instances (157, 159). The tasks (334, 336) in endpoints(342, 344) are different. The task (334) in endpoint (342) is identifiedby the task ID (249) of application (157), and the task (336) inendpoint (344) is identified by the task ID (251) of application (159).The clients (304, 305) in endpoints (342, 344) are different, separateclients. Client (304) in endpoint (342) associates data communicationsresources (e.g., 294, 296, 314 on FIG. 7) dedicated exclusively to theuse of application (157), while client (305) in endpoint (344)associates data communications resources dedicated exclusively to theuse of application (159). Contexts (310, 312) in endpoints (342, 344)are different, separate contexts. Context (310) in endpoint (342)operates on behalf of application (157) a subset of the datacommunications resources of client (304), and context (312) in endpoint(344) operates on behalf of application (159) a subset of the datacommunications resources of client (305).

Contrasted with the PAMIs (218) on compute node (153), the PAMI (218) oncompute node (152) serves only one instance of a parallel application(158) with two endpoints (338, 340). The tasks (332, 333) in endpoints(338, 340) are the same, because they both represent a same instance ofa same application (158); both tasks (332,333) therefore are identified,either with a same variable value, references to a same object, or thelike, by the task ID (250) of application (158). The clients (302, 303)in endpoints (338, 340) are optionally either different, separateclients or the same client. If they are different, each associates aseparate collection of data communications resources. If they are thesame, then each client (302, 303) in the PAMI (218) on compute node(152) associates a same set of data communications resources and isidentified with a same value, object reference, or the like. Contexts(290, 292) in endpoints (338, 340) are different, separate contexts.Context (290) in endpoint (338) operates on behalf of application (158)a subset of the data communications resources of client (302) regardlesswhether clients (302, 303) are the same client or different clients, andcontext (292) in endpoint (340) operates on behalf of application (158)a subset of the data communications resources of client (303) regardlesswhether clients (302, 303) are the same client or different clients.Thus the tasks (332, 333) are the same; the clients (302, 303) can bethe same; and the endpoints (338, 340) are distinguished at least bydifferent contexts (290, 292), each of which operates on behalf of oneof the threads (251-254) of application (158), identified typically by acontext offset or a threading point.

Endpoints (338, 340) being as they are on the same compute node (152)can effect DMA data transfers between endpoints (338, 340) through DMAcontroller (225) and a segment of shared local memory (227). In theabsence of such shared memory (227), endpoints (338, 340) can effect DMAdata transfers through the DMA controller (225) and the network (108),even though both endpoints (338, 340) are on the same compute node(152). DMA transfers between endpoint (340) on compute node (152) andendpoint (344) on another compute node (153) go through DMA controllers(225, 226) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (338) on compute node (152)and endpoint (342) on another compute node (153) also go through DMAcontrollers (225, 226) and either a network (108) or a segment of sharedremote memory (346). The segment of shared remote memory (346) is acomponent of a Non-Uniform Memory Access (‘NUMA’) architecture, asegment in a memory module installed anywhere in the architecture of aparallel computer except on a local compute node. The segment of sharedremote memory (346) is ‘remote’ in the sense that it is not installed ona local compute node. A local compute node is ‘local’ to the endpointslocated on that particular compute node. The segment of shared remotememory (346), therefore, is ‘remote’ with respect to endpoints (338,340) on compute node (158) if it is in a memory module on compute node(153) or anywhere else in the same parallel computer except on computenode (158).

Endpoints (342, 344) being as they are on the same compute node (153)can effect DMA data transfers between endpoints (342, 344) through DMAcontroller (226) and a segment of shared local memory (348). In theabsence of such shared memory (348), endpoints (342, 344) can effect DMAdata transfers through the DMA controller (226) and the network (108),even though both endpoints (342, 344) are on the same compute node(153). DMA transfers between endpoint (344) on compute node (153) andendpoint (340) on another compute node (152) go through DMA controllers(226, 225) and either a network (108) or a segment of shared remotememory (346). DMA transfers between endpoint (342) on compute node (153)and endpoint (338) on another compute node (158) go through DMAcontrollers (226, 225) and either a network (108) or a segment of sharedremote memory (346). Again, the segment of shared remote memory (346) is‘remote’ with respect to endpoints (342, 344) on compute node (153) ifit is in a memory module on compute node (158) or anywhere else in thesame parallel computer except on compute node (153).

For further explanation, FIG. 11 sets forth a flow chart illustrating anexample method of data communications in a PAMI of a parallel computeraccording to embodiments of the present invention. FIG. 12 sets forth acalling sequence diagram further illustrating the operations of themethod of FIG. 11, an example of data communications in a PAMI of aparallel computer according to embodiments of the present invention. Themethod of FIG. 11 is described below in this specification, therefore,with reference both to FIG. 11 and also to FIG. 12, using referencenumbers from both FIGS. 11 and 12.

The method of FIG. 11 is implemented in a PAMI (218) of a parallelcomputer composed of a number of compute nodes (102 on FIG. 1) thatexecute a parallel application, like those described above in thisspecification with reference to FIGS. 1-10. The PAMI (218) includes datacommunications endpoints (352, 354, and, e.g., 338, 340, 342, 344 onFIG. 10), with each endpoint specifying data communications parametersfor a thread (251) of execution on a compute node, includingspecifications of a client, a context, and a task, all as describedabove in this specification with reference to FIGS. 1-10. The endpointsare coupled for data communications through the PAMI (218) and throughdata communications resources (e.g., 238, 240, 242, 246, 106, 108, 227on FIG. 8A). The endpoints can be located on the same compute node or ondifferent compute nodes.

The method of FIG. 11 includes receiving (364) in an origin endpoint(352) of the PAMI (218) a SEND instruction (390). An originatingapplication (158), which is typically one instance of a parallelapplication running on a compute node, places a quantity of transferdata at a location in its RAM represented here as the source buffer(546). The SEND instruction (390) then is received in the originendpoint (352) through operation of a post function called by theoriginating application (158) on a context of the origin endpoint (352),posting the SEND instruction (390) to a work queue of the context. TheSEND instruction (390) specifies a transmission of transfer data fromthe origin endpoint (352) to a first target endpoint (354). In additionto the source buffer (546), the SEND instruction also specifies a SENDdone callback function (391) for the SEND instruction. The SEND donecallback is registered in the PAMI (218) for later use. The SEND donecallback function (391) is an application-level instruction called by anadvance function of the PAMI when execution of the SEND instruction isfully complete. The SEND done callback (391) can carry out any actionsdesired by the application at that point, but readers will recognizethat a typical purpose of the done callback is to advise the callingapplication of completion of the data transfer pursuant to the SENDinstruction. The application's post of the SEND instruction isnon-blocking, so that the application continues other work while thePAMI executes the SEND instruction. Not blocking to wait for the SENDinstruction to complete, it is common for the application to want acallback to advise of completion of the data transfer pursuant to theSEND.

The method of FIG. 11 also includes transmitting (368) from the originendpoint (352) to the first target endpoint (354) a Request-To-Send(‘RTS’) message (369) advising the first target endpoint of the locationand size of the transfer data in the source buffer (546).

The RTS message (394) specifies a dispatch callback function (396) to becalled upon dispatch, that is, upon receipt of the RTS by the firsttarget endpoint (354). The RTS is transmitted by action of an advancefunction called on a context of the origin endpoint (352). The RTS isreceived by action of an advance function, called by an application on acontext of the first target endpoint (354), checking its datacommunications resources for incoming messages, discovering the RTS, andexecuting the dispatch callback (396) specified by the RTS.

The method of FIG. 11 also includes assigning (370) by the first targetendpoint (354) to each of a plurality of target endpoints (564) separateportions (405, 406, 407) of the transfer data. In any given transfer,there are a certain number of target endpoints in the plurality oftarget endpoints that will carry out the transfer. The work of assigningseparate portions is carried out by the RTS's dispatch callback function(396), which divides the size of the transfer data in the source bufferby the number of target endpoints that will participate in the transferand then posting (266) by an advance function (481) of the first targetendpoint (354) receive instructions (542) into work queues (282) ofcontexts (512) of the plurality of target endpoints (564). Each receiveinstruction (542) specifies a separate portion (405, 406, 407) of thetransfer data by a pointer to a separate sub-portion of the sourcebuffer and a size of the sub-portion assigned to each of the pluralityof target endpoints. That is, specifications of separate portions of thetransfer data are implemented with subsidiary beginning addresses forsegments of the source buffer and data sizes for subsidiary quantitiesof data to be received by each of the plurality of target endpoints. Inthis particular example, the number of target endpoints in the plurality(564) is three; the source buffer (546) is divided into three segments(1, 2, 3); and the first target endpoint posts three receiveinstructions into three work queues in three contexts of the threetarget endpoints (354, 560, 562) in the plurality (564). The firsttarget endpoint (354) posts one of the receive instructions into its ownwork queue in its own context, thereby including itself among theplurality of target endpoints that will execute the transfer.

The method of FIG. 11 also includes receiving (376) by the plurality(564) of target endpoints the transfer data. In this example, the firsttarget endpoint has designated itself as one of the plurality of targetendpoints to receive the transfer data. This is optional; in otherembodiments the target endpoint does not designate itself as one of theplurality of target endpoints to receive the data. In the method of FIG.11, receiving (376) the transfer data is carried out by receiving byeach of the plurality (564) of target endpoints the separate portion(405, 406, 407) of the transfer data assigned to that target endpoint.

A dispatch callback (396) of the RTS (394) posts (266) the receiveinstructions (542) to work queues (282) in contexts of the plurality oftarget endpoints that carry out the actual data transfers, and advancefunctions (482) of the contexts (512) in the plurality of targetendpoints execute the receive instructions. The receive instructions canbe implemented as canonical rendezvous receives, of course, with datapackets coursing back and forth across a packet-switching network, butit is probably preferred, when DMA functionality is available, toimplement the receive instructions (542) with DMA GET-type instructions,either through segments of shared memory or across a network, conveyingdata transfers (405, 406, 407) from segments (1, 2, 3) of the sourcebuffer (546) directly from PAMI memory of origin endpoint (352) to thetarget buffer (548), which is PAMI memory of the first target endpoint.

Each advance function (482) in the plurality of target endpoints in thisexample executes in a separate thread (251) of execution. Inembodiments, one thread can advance all contexts, more than one threadcan advance more than one context, or, as here, a separate thread isassigned to advance each context separately. In environments withsufficient resources, it is probably preferred for maximizing theadvantages of parallelism that each advance function is run on aseparate hardware thread or even a separate compute core, therebyliterally running exactly in parallel, at exactly the same time insteadof merely in separate quanta of time on a same core, hardware thread, orsoftware thread.

When the SEND dispatch callback (396) posts the receive instructions tothe plurality of target endpoints (564), the callback (396) includes aan instruction parameter the number of target endpoints (564)participating in the transfer and resets an atomic counter (412) at amemory location accessible by all of the participating target endpoints(564), also advising through an instruction parameter the memory addressof the atomic counter (412). The counter is said to be ‘atomic’ becauseit increments and returns its new counter value in a single atomicoperation, preventing race conditions in reading the counter value. TheSEND dispatch callback (396) also configures each of the posted receiveinstructions with its own done callback (550, 552, 408), so that eachtarget endpoint (564) can determine upon completing its portion of thetransfer whether the entire transfer is complete. When the overalltransfer is complete, the counter value will be ‘3,’ corresponding tothe number of target endpoints participating in the overall transfer.Each target endpoint (564) executes (542) its own separate portion (405,406, 407) of the overall transfer and then increments-and-reads (554,556, 558) the atomic counter (412).

In this example, the sub-transfers are completed in order. Targetendpoint (354) completes its transfer (405), calls its done callback(550) which increments-and-reads (554) atomically the counter value,finds the counter value to be ‘1,’ and simply exits. Target endpoint(560) completes its transfer (406), calls its done callback (552) whichincrements-and-reads (556) atomically the counter value, finds thecounter value to be ‘2,’ and simply exits. Target endpoint (562)completes its transfer (407), calls its done callback (408) whichincrements-and-reads (558) atomically the counter value, finds thecounter value to be ‘3’ (signifying completion of the overall transfer),and calls the transfer done callback (397), a previously registered donecallback for the overall transfer. The transfer done callback (397)notifies (410) the receiving application (159) of overall transfercompletion for the SEND (390) and returns an acknowledgement message(416) to the origin endpoint (352). An advance function of the originendpoint, routinely monitoring its assigned data communicationsresources, finds the incoming acknowledgement message (416) and calls(420) its corresponding SEND done callback function (391). The SEND donecallback function (391) advises (421) the originating application (158)of completion of the overall SEND (390) data transfer.

For further explanation, FIG. 13 sets forth a flow chart illustrating afurther example method of data communications in a PAMI of a parallelcomputer according to embodiments of the present invention. The methodof FIG. 13 is similar to the method of FIG. 11, including as it doesreceiving (364) in an origin endpoint of the PAMI (218) a SENDinstruction (390), transmitting (368) from the origin endpoint to thefirst target endpoint (354) a Request-To-Send (‘RTS’) message (369)advising the first target endpoint of the location and size of thetransfer data in the source buffer (546), assigning (370) by the firsttarget endpoint (354) to each of a plurality of target endpoints (564)separate portions of the transfer data, and receiving (372) by theplurality (564) of target endpoints the transfer data. Like the methodof FIG. 11, the method of FIG. 13 also is implemented in a PAMI (218) ofa parallel computer composed of a number of compute nodes (102 onFIG. 1) that execute a parallel application, like those described abovein this specification with reference to FIGS. 1-10.

The method of FIG. 13, however, also includes dividing (369) by thefirst target endpoint (354) the transfer data in the source buffer (546)into a number of portions, here six portions (1-6), that is larger thanthe number of target endpoints in the plurality of target endpoints,which here is two target endpoints (560, 562). In the method of FIG. 13,assigning (370) portions of the transfer data is carried out byiteratively assigning (371) by the first target endpoint to each of theplurality (560, 562) of target endpoints additional separate portions(374) of the transfer data until all the transfer data is assigned amongthe plurality of target endpoints. Here the iteration is, for example:

-   -   Assign source buffer (546) segment 1 to target endpoint (560) as        an additional separate portion (374) of the transfer data by        posting to work queue (282) of context (512) a DMA GET        instruction, here labeled ‘1’ in the work queue (282). This DMA        GET specifies the beginning address of source buffer segment 1        and the quantity of data in segment 1.    -   Assign source buffer (546) segment 2 to target endpoint (562) as        an additional separate portion (374) of the transfer data by        posting to work queue (283) of context (513) a DMA GET        instruction, here labeled ‘2.’ This DMA GET specifies the        beginning address of source buffer segment 2 and the quantity of        data in segment 2.    -   Assign source buffer (546) segment 3 to target endpoint (560) as        an additional separate portion (374) of the transfer data by        posting to work queue (282) of context (512) a DMA GET        instruction, here labeled ‘3’ in the work queue (282). This DMA        GET specifies the beginning address of source buffer segment 3        and the quantity of data in segment 3.    -   Assign source buffer (546) segment 4 to target endpoint (562) as        an additional separate portion (374) of the transfer data by        posting to work queue (283) of context (513) a DMA GET        instruction, here labeled ‘4.’ This DMA GET specifies the        beginning address of source buffer segment 4 and the quantity of        data in segment 4.    -   Assign source buffer (546) segment 5 to target endpoint (560) as        an additional separate portion (374) of the transfer data by        posting to work queue (282) of context (512) a DMA GET        instruction, here labeled ‘5’ in the work queue (282). This DMA        GET specifies the beginning address of source buffer segment 5        and the quantity of data in segment 5.    -   Assign source buffer (546) segment 6 to target endpoint (562) as        an additional separate portion (374) of the transfer data by        posting to work queue (283) of context (513) a DMA GET        instruction, here labeled ‘6.’ This DMA GET specifies the        beginning address of source buffer segment 6 and the quantity of        data in segment 6.

Also in the method of FIG. 13, receiving the transfer data is carriedout by iteratively receiving (372) by each of the plurality of targetendpoints (560, 562) all of the additional separate portions of thetransfer data assigned to that target endpoint. Iteratively receiving(372) by each of the plurality of target endpoints (560, 562) all of theadditional separate portions of the transfer data assigned to thattarget endpoint is accomplished in this example by advance functions(482, 483) of the plurality of target endpoints (560, 562) finding eacha sequence of receive instructions in their work queues (282, 283)represented in this example by DMA GETs 1, 3, 5 and 2, 4, 6 respectivelyand executing those DMA GET instructions in sequence.

Example embodiments of the present invention are described largely inthe context of a fully functional parallel computer that processes datacommunications in a PAMI. Readers of skill in the art will recognize,however, that the present invention also may be embodied in a computerprogram product disposed upon computer readable storage media for usewith any suitable data processing system. Such computer readable storagemedia may be any storage medium for machine-readable information,including magnetic media, optical media, or other suitable media.Examples of such media include magnetic disks in hard drives ordiskettes, compact disks for optical drives, magnetic tape, and othersas will occur to those of skill in the art. Persons skilled in the artwill immediately recognize that any computer system having suitableprogramming means will be capable of executing the steps of the methodof the invention as embodied in a computer program product. Personsskilled in the art will recognize also that, although some of theexample embodiments described in this specification are oriented tosoftware installed and executing on computer hardware, nevertheless,alternative embodiments implemented as firmware or as hardware are wellwithin the scope of the present invention.

As will be appreciated by those of skill in the art, aspects of thepresent invention may be embodied as method, apparatus or system, orcomputer program product. Accordingly, aspects of the present inventionmay take the form of an entirely hardware embodiment or an embodimentcombining software and hardware aspects (firmware, resident software,micro-code, microcontroller-embedded code, and the like) that may allgenerally be referred to herein as a “circuit,” “module,” “system,” or“apparatus.” Furthermore, aspects of the present invention may take theform of a computer program product embodied in one or more computerreadable media having computer readable program code embodied thereon.

Any combination of one or more computer readable media may be utilized.Such a computer readable medium may be a computer readable signal mediumor a computer readable storage medium. A computer readable storagemedium may be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of the foregoing. More specificexamples (a non-exhaustive list) of the computer readable storage mediumwould include the following: an electrical connection having one or morewires, a portable computer diskette, a hard disk, a random access memory(RAM), a read-only memory (ROM), an erasable programmable read-onlymemory (EPROM or Flash memory), an optical fiber, a portable compactdisc read-only memory (CD-ROM), an optical storage device, a magneticstorage device, or any suitable combination of the foregoing. In thecontext of this document, a computer readable storage medium may be anytangible medium that can contain, or store a program for use by or inconnection with an instruction execution system, apparatus, or device.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electro-magnetic, optical, or any suitable combination thereof. Acomputer readable signal medium may be any computer readable medium thatis not a computer readable storage medium and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device. Program codeembodied on a computer readable medium may be transmitted using anyappropriate medium, including but not limited to wireless, wireline,optical fiber cable, RF, etc., or any suitable combination of theforegoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described in this specificationwith reference to flowchart illustrations and/or block diagrams ofmethods, apparatus (systems) and computer program products according toembodiments of the invention. It will be understood that each block ofthe flowchart illustrations and/or block diagrams, and combinations ofblocks in the flowchart illustrations and/or block diagrams, can beimplemented by computer program instructions. These computer programinstructions may be provided to a processor of a general purposecomputer, special purpose computer, or other programmable dataprocessing apparatus to produce a machine, such that the instructions,which execute via the processor of the computer or other programmabledata processing apparatus, create means for implementing thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowcharts and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof computer apparatus, methods, and computer program products accordingto various embodiments of the present invention. In this regard, eachblock in a flowchart or block diagram may represent a module, segment,or portion of code, which comprises one or more executable instructionsfor implementing the specified logical function(s). It should also benoted that, in some alternative implementations, the functions noted inthe block may occur out of the order noted in the figures. For example,two blocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustrations,and combinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

It will be understood from the foregoing description that modificationsand changes may be made in various embodiments of the present inventionwithout departing from its true spirit. The descriptions in thisspecification are for purposes of illustration only and are not to beconstrued in a limiting sense. The scope of the present invention islimited only by the language of the following claims.

1. A method of data communications in a parallel active messaginginterface (‘PAMI’) of a parallel computer, the parallel computercomprising a plurality of compute nodes that execute a parallelapplication, the PAMI comprising data communications endpoints, eachendpoint comprising a specification of data communications parametersfor a thread of execution on a compute node, including specifications ofa client, a context, and a task, the compute nodes and the endpointscoupled for data communications through the PAMI and through datacommunications resources, the method comprising: receiving in an originendpoint of the PAMI a SEND instruction, the SEND instruction specifyinga transmission of transfer data from the origin endpoint to a firsttarget endpoint; transmitting from the origin endpoint to the firsttarget endpoint a Request-To-Send (‘RTS’) message advising the firsttarget endpoint of the location and size of the transfer data; assigningby the first target endpoint to each of a plurality of target endpointsseparate portions of the transfer data; and receiving by the pluralityof target endpoints the transfer data.
 2. The method of claim 1 whereinreceiving the transfer data further comprises: receiving by each of theplurality of target endpoints the separate portion of the transfer dataassigned to that target endpoint.
 3. The method of claim 1 wherein: themethod further comprises dividing by the first target endpoint thetransfer data into a number of portions that is larger than the numberof target endpoints in the plurality of target endpoints; assigningportions of the transfer data further comprises iteratively assigning bythe first target endpoint to each of the plurality of target endpointsadditional separate portions of the transfer data until all the transferdata is assigned among the plurality of target endpoints; and receivingthe transfer data further comprises iteratively receiving by each of theplurality of target endpoints all of the additional separate portions ofthe transfer data assigned to that target endpoint.
 4. The method ofclaim 1 wherein: assigning separate portions of the transfer datacomprises posting by an advance function of the first target endpointreceive instructions into work queues of contexts of the plurality oftarget endpoints, each receive instruction specifying a separate portionof the transfer data; and receiving the transfer data comprisesexecuting, by advance functions of the contexts in the plurality oftarget endpoints, the receive instructions.
 5. The method of claim 1wherein the receiving of the transfer data is implemented by advancefunctions of contexts in the plurality of endpoints; and each advancefunction in the plurality of target endpoints executes in a separatethread of execution.
 6. The method of claim 1 wherein: each clientcomprises a collection of data communications resources dedicated to theexclusive use of an application-level data processing entity; eachcontext comprises a subset of the collection of data processingresources of a client, context functions, and a work queue of datatransfer instructions to be performed by use of the subset through thecontext functions operated by an assigned thread of execution; and eachtask represents a process of execution of the parallel application. 7.The method of claim 1 wherein each context carries out, through post andadvance operations, data communications for the application on datacommunications resources in the exclusive possession of that context. 8.The method of claim 1 wherein each context carries out datacommunications operations independently and in parallel with othercontexts.
 9. A parallel computer that processes data communications in aparallel active messaging interface (‘PAMI’) of a parallel computer, theparallel computer comprising a plurality of compute nodes that execute aparallel application, the PAMI comprising data communications endpoints,each endpoint comprising a specification of data communicationsparameters for a thread of execution on a compute node, includingspecifications of a client, a context, and a task, the compute nodes andthe endpoints coupled for data communications through the PAMI andthrough data communications resources, the compute nodes comprisingcomputer processors operatively coupled to computer memory havingdisposed within it computer program instructions that, when executed bythe computer processors, cause the parallel computer to function by:receiving in an origin endpoint of the PAMI a SEND instruction, the SENDinstruction specifying a transmission of transfer data from the originendpoint to a first target endpoint; transmitting from the originendpoint to the first target endpoint a Request-To-Send (‘RTS’) messageadvising the first target endpoint of the location and size of thetransfer data; assigning by the first target endpoint to each of aplurality of target endpoints separate portions of the transfer data;and receiving by the plurality of target endpoints the transfer data.10. The parallel computer of claim 9 wherein receiving the transfer datafurther comprises: receiving by each of the plurality of targetendpoints the separate portion of the transfer data assigned to thattarget endpoint.
 11. The parallel computer of claim 9 wherein: thecomputer memory further has disposed within it computer programinstructions that, when executed by the computer processors, cause theparallel computer to function by dividing by the first target endpointthe transfer data into a number of portions that is larger than thenumber of target endpoints in the plurality of target endpoints;assigning portions of the transfer data further comprises iterativelyassigning by the first target endpoint to each of the plurality oftarget endpoints additional separate portions of the transfer data untilall the transfer data is assigned among the plurality of targetendpoints; and receiving the transfer data further comprises iterativelyreceiving by each of the plurality of target endpoints all of theadditional separate portions of the transfer data assigned to thattarget endpoint.
 12. The parallel computer of claim 9 wherein: assigningseparate portions of the transfer data comprises posting by an advancefunction of the first target endpoint receive instructions into workqueues of contexts of the plurality of target endpoints, each receiveinstruction specifying a separate portion of the transfer data; andreceiving the transfer data comprises executing, by advance functions ofthe contexts in the plurality of target endpoints, the receiveinstructions.
 13. The parallel computer of claim 9 wherein the receivingof the transfer data is implemented by advance functions of contexts inthe plurality of endpoints; and each advance function in the pluralityof target endpoints executes in a separate thread of execution.
 14. Theparallel computer of claim 9 wherein: each client comprises a collectionof data communications resources dedicated to the exclusive use of anapplication-level data processing entity; each context comprises asubset of the collection of data processing resources of a client,context functions, and a work queue of data transfer instructions to beperformed by use of the subset through the context functions operated byan assigned thread of execution; and each task represents a process ofexecution of the parallel application.
 15. A computer program productfor processing data communications in a parallel active messaginginterface (‘PAMI’) of a parallel computer, the parallel computercomprising a plurality of compute nodes that execute a parallelapplication, the PAMI comprising data communications endpoints, eachendpoint comprising a specification of data communications parametersfor a thread of execution on a compute node, including specifications ofa client, a context, and a task, the compute nodes and the endpointscoupled for data communications through the PAMI and through datacommunications resources, the computer program product disposed upon acomputer readable storage medium, the computer program productcomprising computer program instructions that, when installed andexecuted, cause the parallel computer to function by: receiving in anorigin endpoint of the PAMI a SEND instruction, the SEND instructionspecifying a transmission of transfer data from the origin endpoint to afirst target endpoint; transmitting from the origin endpoint to thefirst target endpoint a Request-To-Send (‘RTS’) message advising thefirst target endpoint of the location and size of the transfer data;assigning by the first target endpoint to each of a plurality of targetendpoints separate portions of the transfer data; and receiving by theplurality of target endpoints the transfer data.
 16. The computerprogram product of claim 15 wherein receiving the transfer data furthercomprises: receiving by each of the plurality of target endpoints theseparate portion of the transfer data assigned to that target endpoint.17. The computer program product of claim 15 wherein: the computerprogram product further comprises computer program instructions that,when installed and executed, cause the parallel computer to function bydividing by the first target endpoint the transfer data into a number ofportions that is larger than the number of target endpoints in theplurality of target endpoints; assigning portions of the transfer datafurther comprises iteratively assigning by the first target endpoint toeach of the plurality of target endpoints additional separate portionsof the transfer data until all the transfer data is assigned among theplurality of target endpoints; and receiving the transfer data furthercomprises iteratively receiving by each of the plurality of targetendpoints all of the additional separate portions of the transfer dataassigned to that target endpoint.
 18. The computer program product ofclaim 15 wherein: assigning separate portions of the transfer datacomprises posting by an advance function of the first target endpointreceive instructions into work queues of contexts of the plurality oftarget endpoints, each receive instruction specifying a separate portionof the transfer data; and receiving the transfer data comprisesexecuting, by advance functions of the contexts in the plurality oftarget endpoints, the receive instructions.
 19. The computer programproduct of claim 15 wherein the receiving of the transfer data isimplemented by advance functions of contexts in the plurality ofendpoints; and each advance function in the plurality of targetendpoints executes in a separate thread of execution.
 20. The computerprogram product of claim 15 wherein: each client comprises a collectionof data communications resources dedicated to the exclusive use of anapplication-level data processing entity; each context comprises asubset of the collection of data processing resources of a client,context functions, and a work queue of data transfer instructions to beperformed by use of the subset through the context functions operated byan assigned thread of execution; and each task represents a process ofexecution of the parallel application.